Learning Kernel Classifiers
Theory and Algorithms
By Ralf Herbrich
"Learning Kernel Classifiers" is published by MIT Press in 2018 - Cambridge, Mass, it has 384 pages and the language of the book is English.
“Learning Kernel Classifiers” Metadata:
- Title: Learning Kernel Classifiers
- Author: Ralf Herbrich
- Language: English
- Number of Pages: 384
- Publisher: MIT Press
- Publish Date: 2018
- Publish Location: Cambridge, Mass
“Learning Kernel Classifiers” Subjects and Themes:
- Subjects: ➤ Algorithms - Machine learning - Algorithmes - Aprendizado computacional - Apprentissage automatique - Algoritmen - Maschinelles Lernen - Machine-learning - Klassifikator (Informatik) - Algoritmos - Kernel (Informatik) - COMPUTERS - Enterprise Applications - Business Intelligence Tools - Intelligence (AI) & Semantics
Edition Identifiers:
- The Open Library ID: OL29023371M - OL9291621W
- ISBN-13: 9780262256339 - 9780585436685 - 9780262083065
- All ISBNs: 9780262256339 - 9780585436685 - 9780262083065
AI-generated Review of “Learning Kernel Classifiers”:
"Learning Kernel Classifiers" Description:
Open Data:
Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library
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